Anslysis objective: To see if there is realtionship between cell wall chemistry and cell wall resistance (rwp or Resistwp25) or Gm (conductance).

Problems: 1) the cell wall chemistry was taken from trunk material while Rm and rwp are calculated from leaves. 2) cell wall data were collected from various outside commongarden sites while leaf physiology was collected from greenhouse material.

setwd("~/Desktop/poplar_data")
library(dplyr)
## 
## Attaching package: 'dplyr'
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
dat1<-read.csv("datainput_2_27_2015.csv")
head(dat1)
names(dat1)

dat1$id<-toupper(dat1$id) #all letters upper case
dat1$id<-gsub("*.CSV", "", dat1$id) # replace .csv to blank
head(dat1)
str(dat1)

datRecal<-read.csv("CLA_Recalci_Input.csv")
datCorv<-read.csv("Corvallis_2010.csv")
datPop<-read.csv("Poplar_C5_C6_12_20_11.csv")

getting counts for number of rch and rwp > 0

a<-dat1 %>% select(id, Resistch25, Resistwp25) %>% filter(Resistwp25 > 0)
b<-dat1 %>% select(id, Resistch25, Resistwp25) %>% filter(Resistch25 > 0)

Data manipulation and Gm calculation Note: can only caluclate Gm for samples that have rwp > 0.

dat2<-dat1 %>% select(id, Duplicate, Jmax25, Rdlight25, Resistwp25, Resistch25, gamma.25, Asat_ALight) %>% filter(Duplicate == "Unique" & Resistwp25 > 0) %>% mutate(Gm = 1/Resistwp25)

Investigate relationships or rwp (Resistwp25) with cell wall chemistry

File names for the wall chemistry: CLA_SG_Input.csv CLA_C5_6.csv CLA_Recalci_Input.csv Corvallis_2010.csv

Plots for the Clatskine Site

datRecal_join<-inner_join(dat2, datRecal, by = "id")
## Warning: joining character vector and factor, coercing into character
## vector
plot(datRecal_join$Lignin, datRecal_join$Resistwp25)

cor.test(datRecal_join$Lignin, datRecal_join$Resistwp25, method = "spearm")
## Warning in cor.test.default(datRecal_join$Lignin,
## datRecal_join$Resistwp25, : Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  datRecal_join$Lignin and datRecal_join$Resistwp25
## S = 8100122, p-value = 0.1988
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## -0.068173
cor.test(datRecal_join$Lignin, datRecal_join$Resistwp25, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  datRecal_join$Lignin and datRecal_join$Resistwp25
## t = -1.9061, df = 355, p-value = 0.05745
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.202331733  0.003179182
## sample estimates:
##        cor 
## -0.1006498
plot(datRecal_join$Lignin, datRecal_join$Gm, ylim=c(0,2))

cor.test(datRecal_join$Lignin, datRecal_join$Gm, method = "spearm")
## Warning in cor.test.default(datRecal_join$Lignin, datRecal_join$Gm, method
## = "spearm"): Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  datRecal_join$Lignin and datRecal_join$Gm
## S = 7066190, p-value = 0.1988
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##      rho 
## 0.068173
cor.test(datRecal_join$Lignin, datRecal_join$Gm, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  datRecal_join$Lignin and datRecal_join$Gm
## t = 0.8018, df = 355, p-value = 0.4232
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.06155286  0.14566742
## sample estimates:
##        cor 
## 0.04251449
plot(datRecal_join$SG, datRecal_join$Resistwp25)

plot(datRecal_join$SG, datRecal_join$Gm, ylim=c(0,2))

plot(datRecal_join$Glucose, datRecal_join$Resistwp25)

plot(datRecal_join$Glucose, datRecal_join$Gm, ylim=c(0,2))

plot(datRecal_join$Xylose, datRecal_join$Resistwp25)

plot(datRecal_join$Xylose, datRecal_join$Gm, ylim=c(0,2))

plot(datRecal_join$ GlucoseXylose, datRecal_join$Resistwp25)

plot(datRecal_join$ GlucoseXylose, datRecal_join$Gm, ylim=c(0,2))

Plots for Corvallis

Note, not many points in Corvallis_2010.csv

datCorv_join<-inner_join(dat2, datCorv, by = "id")
## Warning: joining character vector and factor, coercing into character
## vector
plot(datCorv_join$Lignin, datCorv_join$Resistwp25)

cor.test(datCorv_join$Lignin, datCorv_join$Resistwp25, method = "spearm")
## Warning in cor.test.default(datCorv_join$Lignin, datCorv_join$Resistwp25,
## : Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  datCorv_join$Lignin and datCorv_join$Resistwp25
## S = 70808.22, p-value = 0.7837
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.03201336
cor.test(datCorv_join$Lignin, datCorv_join$Resistwp25, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  datCorv_join$Lignin and datCorv_join$Resistwp25
## t = 0.5486, df = 74, p-value = 0.5849
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1641698  0.2850075
## sample estimates:
##        cor 
## 0.06364132
plot(datCorv_join$Lignin, datCorv_join$Gm, ylim=c(0,2))

cor.test(datCorv_join$Lignin, datCorv_join$Gm, method = "spearm")
## Warning in cor.test.default(datCorv_join$Lignin, datCorv_join$Gm, method =
## "spearm"): Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  datCorv_join$Lignin and datCorv_join$Gm
## S = 75491.78, p-value = 0.7837
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.03201336
cor.test(datCorv_join$Lignin, datCorv_join$Gm, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  datCorv_join$Lignin and datCorv_join$Gm
## t = 0.4119, df = 74, p-value = 0.6816
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.1795674  0.2703647
## sample estimates:
##        cor 
## 0.04782431
plot(datCorv_join$S_G, datCorv_join$Resistwp25)

plot(datCorv_join$S_G, datCorv_join$Gm, ylim=c(0,2))

plot(datCorv_join$Glucoserelease, datCorv_join$Resistwp25)

plot(datCorv_join$Glucoserelease, datCorv_join$Gm, ylim=c(0,2))

plot(datCorv_join$Xyloserelease, datCorv_join$Resistwp25)

plot(datCorv_join$Xyloserelease, datCorv_join$Gm, ylim=c(0,2))

plot(datCorv_join$GlucoseXyloserelease, datCorv_join$Resistwp25)

plot(datCorv_join$GlucoseXyloserelease, datCorv_join$Gm, ylim=c(0,2))

the Poplar_C5_C6_12_20_11.csv dataset

details unknown

datPop_join<-inner_join(dat2, datPop, by = "id")
## Warning: joining character vector and factor, coercing into character
## vector
plot(datPop_join$Lignin, datPop_join$Resistwp25)

cor.test(datPop_join$Lignin, datPop_join$Resistwp25, method = "spearm")
## Warning in cor.test.default(datPop_join$Lignin, datPop_join$Resistwp25, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  datPop_join$Lignin and datPop_join$Resistwp25
## S = 2211919, p-value = 0.1786
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.08639537
cor.test(datPop_join$Lignin, datPop_join$Resistwp25, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  datPop_join$Lignin and datPop_join$Resistwp25
## t = 0.4128, df = 242, p-value = 0.6801
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.09939061  0.15160709
## sample estimates:
##        cor 
## 0.02652632
plot(datPop_join$Lignin, datPop_join$Gm, ylim=c(0,2))

cor.test(datPop_join$Lignin, datPop_join$Gm, method = "spearm")
## Warning in cor.test.default(datPop_join$Lignin, datPop_join$Gm, method =
## "spearm"): Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  datPop_join$Lignin and datPop_join$Gm
## S = 2630261, p-value = 0.1786
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.08639537
cor.test(datPop_join$Lignin, datPop_join$Gm, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  datPop_join$Lignin and datPop_join$Gm
## t = -0.9101, df = 242, p-value = 0.3637
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.18264806  0.06768036
## sample estimates:
##         cor 
## -0.05840186
plot(datPop_join$S_G, datPop_join$Resistwp25)

plot(datPop_join$S_G, datPop_join$Gm, ylim=c(0,2))

plot(datPop_join$C5, datPop_join$Resistwp25)

plot(datPop_join$C5, datPop_join$Gm, ylim=c(0,2))

plot(datPop_join$C6, datPop_join$Resistwp25)

plot(datPop_join$C6, datPop_join$Gm, ylim=c(0,2))

evaluate through correlagrams data from Poplar_C5_C6_12_20_11.csv

data from Corvallis_2010.csv

data from Clatskine Site